Indicator-based Methods Research Articles

Prioritising and incentivising productivity within indicator-based approaches to Research Impact Assessment: a commentary

Research Impact Assessment (RIA) represents one of a suite of policies intended to improve the impact generated from investment in health and medical research (HMR). Positivist indicator-based approaches to RIA are widely implemented but increasingly criticised as theoretically problematic, unfair, and burdensome. This commentary proposes there are useful outcomes that emerge from the process of applying an indicator-based RIA framework, separate from those encapsulated in the metrics themselves. The aim for this commentary is to demonstrate how the act of conducting an indicator-based approach to RIA can serve to optimise the productive gains from the investment in HMR. Prior research found that the issues regarding RIA are less about the choice of indicators/metrics, and more about the discussions prompted and activities incentivised by the process. This insight provides an opportunity to utilise indicator-based methods to purposely optimise the research impact. An indicator-based RIA framework specifically designed to optimise research impacts should: focus on researchers and the research process, rather than institution-level measures; utilise a project level unit of analysis that provides control to researchers and supports collaboration and accountability; provide for prospective implementation of RIA and the prospective orientation of research; establish a line of sight to the ultimate anticipated beneficiaries and impacts; Include process metrics/indicators to acknowledge interim steps on the pathway to final impacts; integrate ‘next’ users and prioritise the utilisation of research outputs as a critical measure; Integrate and align the incentives for researchers/research projects arising from RIA, with those existing within the prevailing research system; integrate with existing peer-review processes; and, adopt a system-wide approach where incremental improvements in the probability of translation from individual research projects, yields higher impact across the whole funding portfolio.Optimisation of the impacts from HMR investment represents the primary purpose of Research Impact policy. The process of conducting an indicator-based approach to RIA, which engages the researcher during the inception and planning phase, can directly contribute to this goal through improvements in the probability that an individual project will generate interim impacts. The research project funding process represents a promising forum to integrate this approach within the existing research system.

A co-produced national climate change risk and vulnerability assessment framework for South Africa

IntroductionThere are mounting demands to undertake climate risk and vulnerability (CRV) assessments for policy, planning, funding, insurance, and compliance reasons. In Africa, given the adaptation imperative, this is particularly important. Increasingly, it has become clear that sub-national assessments are needed to inform adaptation practice. However, there has been relatively little guidance on how to undertake these more local assessments and aggregate them making it difficult for national governments to know the extent and variability of climate vulnerability and risk across the country.MethodsIn South Africa, the national government, led by the Department of Forestry, Fisheries and the Environment (DFFE), undertook to establish a common framework to guide the development and review of CRV assessments. This paper presents the framework that was co-developed through a series of engagements with stakeholders active in implementing and supporting CRV assessments.ResultsThe framework is intended to provide guidance on what to consider when undertaking CRV assessments within diverse South African contexts in order to enable alignment, comparison, and aggregation between them and work towards an effective climate adaptation response across scales. Rather than standardizing a methodology, the framework promotes the use of a standard set of concepts as the basis for each assessment and profiles a diversity of methods, tools and data sources for applying the concepts in a contextually sensitive way. This provides a flexible yet structured sequence of three interlinked steps in a risk and vulnerability assessment process, namely: (1) Planning, (2) Scoping and (3) Assessing. The framework guides users through the choice and application of three assessment depths, depending on decision-context, resourcing and extent of pre-existing data and information. It encourages the integration of participatory and indicator-based methods through an impact chain approach, profiling more than 30 freely available tools and resources. This process builds a strong evidence base and a deepening set of engagements and shared understanding between relevant stakeholders, upon which to act.DiscussionThis South African process can provide insight and support for actors driving the climate agenda in other countries looking to develop comparable assessments as the basis to drive equitable and transformative climate action and learning.

Better or different? A reflection on the suitability of indicator methods for a just transition to a circular economy

Indicator-based methods have long been used as assessment tools in relation to measuring and purportedly enabling sustainable transitions. Common limitations of indicator approaches are well documented in the literature, and include both technical issues related to data availability and the handling of complexity, and epistemological challenges such as the nature of trade-offs and risks associated with reductionism. Nevertheless, such methods remain popular due to their ability to convey complex information related to timely issues in a synthesised way to policy- and decision-makers. In light of this, and the burgeoning literature on indicators for a Circular Economy (CE), we aim to reflect on the extent to which such methods are suitable for engendering a transformative social and ecological transition to a just CE. To do so, we examine the broad literature on the limitations of indicator methods by considering an archetypal three step process of selection, framing, and implementation. As critical CE scholars keen to repoliticise CE by embedding principles of justice, we ask to what extent indicator methods serve our transformative purposes, and whether our stance towards such methods should be to do things better or different? Our answer to this is: both. Yet we emphasise the need to reconceive ‘better’ as moving beyond fixes to technical problems to address more fundamental epistemological challenges and rethink the purpose of an indicator approach as not a technical tool, but a politicised artefact for shaping alternative narratives.

Analytical Methods for Oxalate Quantification: The Ubiquitous Organic Anion

Oxalate is a divalent organic anion that affects many biological and commercial processes. It is derived from plant sources, such as spinach, rhubarb, tea, cacao, nuts, and beans, and therefore is commonly found in raw or processed food products. Oxalate can also be made endogenously by humans and other mammals as a byproduct of hepatic enzymatic reactions. It is theorized that plants use oxalate to store calcium and protect against herbivory. Clinically, oxalate is best known to be a major component of kidney stones, which commonly contain calcium oxalate crystals. Oxalate can induce an inflammatory response that decreases the immune system's ability to remove renal crystals. When formulated with platinum as oxaliplatin (an anticancer drug), oxalate has been proposed to cause neurotoxicity and nerve pain. There are many sectors of industry that are hampered by oxalate, and others that depend on it. For example, calcium oxalate is troublesome in the pulp industry and the alumina industry as it deposits on machinery. On the other hand, oxalate is a common active component of rust removal and cleaning products. Due to its ubiquity, there is interest in developing efficient methods to quantify oxalate. Over the past four decades, many diverse methods have been reported. These approaches include electrochemical detection, liquid chromatography or gas chromatography coupled with mass spectrometry, enzymatic degradation of oxalate with oxalate oxidase and detection of hydrogen peroxide produced, and indicator displacement-based methods employing fluorescent or UV light-absorbing compounds. Enhancements in sensitivity have been reported for both electrochemical and mass-spectrometry-based methods as recently as this year. Indicator-based methods have realized a surge in interest that continues to date. The diversity of these approaches, in terms of instrumentation, sample preparation, and sensitivity, has made it clear that no single method will work best for every purpose. This review describes the strengths and limitations of each method, and may serve as a reference for investigators to decide which approach is most suitable for their work.

Comparing participatory mapping and a spatial biophysical assessment of ecosystem service cold spots in agricultural landscapes

In this study, we assess the supply of five ecosystem services (ES, i.e. biodiversity provision, carbon sequestration, erosion control, water availability and yield) in an agricultural landscape in Northeast Germany as perceived by different stakeholders with a web-based questionnaire. We complement this participatory approach with a biophysical assessment of the same ES in the same study area using spatially explicit, indicator-based methods. A research gap exists in the combination of participatory and biophysical ES assessment methods within one study area. We derive spots of low supply of multiple ES (cold spots of ES supply) from the areas identified by the mapping and the biophysical assessment, and in collaboration with stakeholders of the region during an online workshop. Our interest is to (i) identify the advantages of comparing and combining biophysical with participatory methods to assess ES and to (ii) identify interfaces where combining both approaches can help to integrate ES assessment in landscape planning, management and design. Our goal is to establish an assessment basis that allows for a spatially explicit representation of trade-offs and synergies of ES by displaying multiple ES in one case study area, capable of integrating different resolutions. By comparing participatory and biophysical assessments, we identify ecological and social benefits of the landscape, and emphasize the social-ecological interface by limiting the scope of the biophysical assessment to the area of interest by the stakeholders. Besides, areas in which participants over- or underestimate the current ES supply are spotted by quantifying the gap between actual and perceived supply. The results reveal several similarities in the observations derived from both assessments. However, water availability is widely underestimated, whereas biodiversity and carbon sequestration are slightly overestimated. Based on our results, we conclude that in many cases, stakeholders who are familiar with the landscape because they live there or have a professional relation to it have a profound understanding of the ongoing ecosystem processes. The decision whether to use participatory, biophysical or both assessment techniques should be made according to the use case: from a governance perspective, participatory data can be easier to communicate and more easily accessible. We encourage the perspective that there are cases in which the low-threshold participatory data provide sufficiently reliable information to make informed decisions on ES management, particularly when biophysical assessment studies are too resource- and cost-intensive.

The impact of the aggregation formula on indicator-based method for the assessment of building susceptibility to hydro-meteorological hazards

A crucial variable in risk and vulnerability assessment procedures is the ability of the built environment to resist in case of hazard impact. This component, referred to as “susceptibility”, can be assessed in different ways, among which indicator-based methods are preferable when no data about past hazardous events are available. This type of methodology, considering the building susceptibility independent from the hazard magnitude, relies on the selection of indicators meaningful for the investigated area and their combination via an aggregation formula, leading to a susceptibility index for each building.The current study aims at reviewing and comparing different approaches used in literature to combine the indicators for calculating the susceptibility index. To this end, five indicators, previously selected for a mountainous case study area, are applied and implemented in a GIS platform, generating just as many specific susceptibility maps. Further, the indicators are weighed and combined in different scenarios with two aggregation formulae, which are integrated into the GIS environment so as to spatially display and compare the results. Last, but not least, the HBIM model and a GIS/BIM database of the studied area are created. Results suggest that the aggregation formula and the assigned weights are crucial ingredients, and therefore they should be chosen carefully according to the aim of the study and the needs of the stakeholders.

On self-adaptive stochastic ranking in decomposition many-objective evolutionary optimization

Evolutionary multi-objective optimization problems have attracted increasingly attention in the evolutionary computing community. Now a lot of efforts have been devoted to this direction. For example, the proposed Pareto-dominated, decomposition-based and indicator-based methods have improved the scalability of multi-objective evolutionary algorithms (MOEAs). However, with the increase of the number of objectives, the portion of non-dominated individuals in the population is too large, and the distinguishability of individuals in the objective space decreases, which will affect the selection of elite solutions, thereby losing the balance of convergence and diversity. In this paper, a self-adaptive stochastic ranking method (SSR) is proposed to adaptively balance the convergence and diversity in high-dimensional space according to the state of the population. It has been embedded into the MOEA/D framework to form a novel multi-objective optimization algorithm, named MOEA/D-SSR. In addition, an improved shift-based density estimation strategy (ISDE) is adopted to enhance the convergence and diversity. Compared with the existing MOEAs on benchmark suite DTLZ and WFG with up to 10 objectives, the performance of the our algorithm has been verified. Experimental results show that the proposed algorithm is competitive compared with the most advanced MOEAs.

Multi-Criteria Data Analysis with Interval-Valued Neutrosophic Sets to Decrease Plastic Pollution rivers

Whether for human consumption, agricultural production, or industrial processes, groundwater, and rivers, in particular, are crucial. The river is a major water source; therefore, we must do all we can to keep it clean. Inconsistencies in data, algorithms and expert judgments have contributed to the growing recognition of ambiguity analysis' significance. However, it is not common practice to include vulnerability assessment in MCA based Interval-Valued Neutrosophic Sets (IVNSs). To better understand what causes MCA uncertainty, this research examines the factors that contribute to it. Probabilistic techniques, indicator-based methods, and neutrosophic logic are examined as examples of broad approaches to analysis methods in MCA. The practicality, financial and ecological consequences, unexpected social and environmental implications, possible scale of change, and confirmation of the impact of plastic reduction strategies were investigated using an MCA technique.

Review of vulnerability indicators for fire risk assessment in cultural heritage

Indicator-based methods are able to provide a simple and reliable way to assess the level of fire risk of existing constructions. While many of these methods were not developed for cultural heritage assets, those that were often involve indicators connected to provisions defined by standards that are only valid in certain countries. As such, a method with the required versatility to analyse the fire safety of cultural heritage assets is still needed. Developing a method with the necessary characteristics requires a prior review of existing approaches to understand their strengths and weaknesses. A review of existing indicator-based methods for fire risk assessment is, therefore, presented, covering methods applicable to built cultural heritage assets and other types of constructions. In addition, the review also analyses existing fire safety guidelines and recommendations dedicated to cultural heritage assets. As an outcome, the review proposes twenty-two vulnerability indicators grouped into four categories that can be used to develop a fire risk assessment method dedicated to built cultural heritage assets. The review also establishes a classification of cultural heritage assets according to four classes that focus on their fire vulnerability and discusses the applicability of the proposed indicators.

CHANGES IN THE COMPETITIVE POSITION OF CHINA AND THE EUROPEAN UNION IN INTERNATIONAL AGRICULTURAL AND FOOD TRADE CONSIDERING AGRICULTURAL WORKFORCE PRODUCTIVITY

This paper aims to evaluate the competitive position of China and the European Union in international agricultural and food trade using properly selected measurement indicators and comparing the resulting values with changes in workforce productivity in agriculture. On foreign markets, an improvement in the competitive position of a country/ regional group is equivalent to an increase in the share of a specific entity in export markets. One of the basic determinants of the scale of regional and global competitiveness is workforce productivity. The period of analysis ranges from 2001 to 2017. Data was retrieved from statistics database FAOSTAT and the World Bank. The analysis used indicator-based methods. The studies carried out endorse the statement that a larger improvement in workforce productivity in agriculture in China compared to EU countries had little influence on the international competitive position of that country compared to EU countries. In the analyzed period, the value of the trade coverage ratio (TCR) is decreasing for China and increasing for the EU, as is the value of the relative export orientation index (REO). In the case of China, the negative balance of trade is deepening, while the EU achieves a positive balance in agricultural and food trade in 2010. The results of analyses point to an improvement in the competitive position of the European Union and a deterioration in the position of China in international agricultural and food trade.

Physical flood vulnerability assessment of buildings in Kota Bharu, Malaysia: an indicator-based approach

Purpose The purpose of this paper is to introduce a geospatial approach for buildings flood vulnerability assessment using an indicator-based method (IBM) to support flood risk assessment and mapping of physical elements at risk in Kota Bharu District, Kelantan, Malaysia. Design/methodology/approach The study developed an indicator-based approach to undertake physical flood vulnerability assessment of buildings. The approach takes into consideration flood hazard intensity, building characteristics and structures surrounding the environment as factors that influence flood vulnerability. The aggregation of the total flood vulnerability index is carried out in a geographic information system (GIS) environment. Findings The results provide a spatial representation of buildings flood vulnerability index in Kota Bharu Malaysia, and the degree of expected vulnerability is expressed on a scale between 0 to 1 (low damage to total damage). Mapping flood vulnerability index of buildings should be considered in future flood mitigation and evacuation planning. Originality/value Unlike other indicator-based methods (IBMs) developed for physical flood vulnerability assessment, in the current study, hazard intensity has been considered and incorporated in the physical flood vulnerability model.

Integrated Sustainability Assessment of Divergent Mediterranean Farming Systems: Cyprus as a Case Study

A variety of indicator-based methods have been developed for the sustainability assessment of farming systems (FSs). However, many of them lack holisticity, focus on a specific agricultural sector/product, and do not provide aggregated results to better support decision-making process. The goal of this study was, for the first time, to assess, in a holistic manner, the sustainability performance of different FSs in southeastern Cyprus. The methodological framework involved three major steps. First, the sustainability context was set, and a list of 41 environmental, social, and economic indicators was created. The indicators were then calculated using data from 324 farms. Second, six FSs were identified using multivariate analysis. Finally, the sustainability of FSs was assessed by combining numerical (construction of four composite sustainability indices) and visual (presentation of indicator scores and values with graphs and tables) integration approaches. While the indices provided the “big picture”, visual integration revealed the areas where policy interventions are needed. The analysis showed that sustainable agricultural practices are already used by some farmers in the area. The results could be used for benchmarking purposes and to aid decision-making process in Cyprus but might also be useful for other Mediterranean regions with similar agro-ecological conditions.

A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows

Remaining useful life (RUL) prediction is a challenging research task in prognostics and receives extensive attention from academia to industry. This paper proposes a novel deep convolutional neural network (CNN) for RUL prediction. Unlike health indicator-based methods which require the long-term tracking of sensor data from the initial stage, the proposed network aims to utilize data from consecutive time samples at any time interval for RUL prediction. Additionally, a new kernel module for prognostics is designed where the kernels are selected automatically, which can further enhance the feature extraction ability of the network. The effectiveness of the proposed network is validated using the C-MAPSS dataset for aircraft engines provided by NASA. Compared with the state-of-the-art results on the same dataset, the prediction results demonstrate the superiority of the proposed network.

The importance of indicator weights for vulnerability indices and implications for decision making in disaster management

Decision makers in the field of disaster management have many tools at their disposal to assist them in making informed decisions. They make use of the available information to set priorities, choose adequate local adaptation measures and make decisions regarding land use and emergency planning. As far as the vulnerability assessment of buildings is concerned, a wide range of methods can be found in the literature. However, some methods (e.g. vulnerability curves) require a large amount of empirical data that are not always available. An alternative solution is the development of vulnerability indices. In this paper, two relative vulnerability indices (PTVA-3 and PTVA-4) designed for the assessment of vulnerability of buildings to tsunamis are applied. The case study area is located in Apulia (Italy). Although no historic record of tsunamis is available for the area, geological investigations have shown that the area has been affected by tsunamis in the past. The two methods use different weighting for the indicators considered for the vulnerability index leading to different results. In this way, the importance of the weighting of indicators and its implication in decision making is shown. Moreover, the study offers the opportunity of an overall assessment of the use of indicators in vulnerability assessment, their advantages and drawbacks as well as potential improvements of indicator-based methods.

A review of multi-objective optimisation and decision making using evolutionary algorithms

Research in the field of multi-objective optimisation problem (MOP) has garnered ample interest in the last two decades. Majority of methods developed for solving the problem belong to the class of evolutionary algorithms (EA) which are population-based evolution search strategies involving exploration and exploitation in general. Multi-criteria decision making (MCDM) is another aspect of MOP which involves finding methods to help a decision maker (DM) in making most optimal decisions in a conflicting scenario. In this paper, we present a brief review of the methods and techniques developed in the last 15 years which try to solve the MOP and MCDM problems. The strengths and weaknesses of methods have been discussed to present a holistic view. This paper covers challenges associated with MOEAs, different solution approaches such as Pareto-based methods and non-Pareto methods, indicator-based methods, aggregation methods, decomposition-based methods, methods using reference sets, MOEAs involving DM, a priori, interactive and a posteriori preference incorporation methods. It also discusses most of the quality metrics and performance indicators proposed in the literature along with benchmark problems. In addition, some future research issues and directions are also presented.

Comparative Analysis of Inhibition-Based and Indicator-Independent Colorimetric Assay for Screening of Bacteriocin-Producing Lactic Acid Bacteria.

Screening of bacteriocin-producing lactic acid bacteria (LAB) is an important aspect for the search of new/novel probiotic strains. Here, a vesicle-based colorimetric assay was compared with conventional inhibition-based antimicrobial assays using 54 isolates of LAB. All isolates demonstrated zone of growth inhibition ranging from 2.5 to 7.5mm against indicator strain, Micrococcus luteus MTCC106 using point inoculation method. Cell-free supernatant of the isolates showed zone of growth inhibition varying from 14.5 to 25mm using agar well diffusion assay. These isolates inhibited the growth of indicator strain by 89.56-98.65%. The antimicrobial activity present in cell-free supernatant of different isolates was found to be in the range of 10-160AUml- 1. The treatment of polydiacetylene (PDA) vesicles with cell-free supernatant of selected isolates led to blue-red color transition, and presence of protein band on tricine SDS-PAGE confirmed the presence of membrane-acting peptides, bacteriocins. The colorimetric responses (CR%) varied between 0 and 59%, and the assay was found to be more sensitive, faster, and reliable as compared to the other conventional indicator-based methods used. Therefore, the colorimetric assay may be specifically applied for screening of bacteriocin-producing LAB.

Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis

Electric vehicle (EV) charging infrastructure rollout is well under way in several power systems, namely North America, Japan, Europe, and China. In order to support EV charging infrastructures design and operation, little attempt has been made to develop indicator-based methods characterising such networks across different regions. This study defines an assessment methodology, composed by eight indicators, allowing a comparison among EV public charging infrastructures. The proposed indicators capture the following: energy demand from EVs, energy use intensity, charger’s intensity distribution, the use time ratios, energy use ratios, the nearest neighbour distance between chargers and availability, the total service ratio, and the carbon intensity as an environmental impact indicator. We apply the methodology to a dataset from ElaadNL, a reference smart charging provider in The Netherlands, using open source geographic information system (GIS) and R software. The dataset reveals higher energy intensity in six urban areas and that 50% of energy supplied comes from 19.6% of chargers. Correlations of spatial density are strong and nearest neighbouring distances range from 1101 to 9462 m. Use time and energy use ratios are 11.21% and 3.56%. The average carbon intensity is 4.44 gCO2eq/MJ. Finally, the indicators are used to assess the impact of relevant public policies on the EV charging infrastructure use and roll-out.

Deterministic and fuzzy-based methods to evaluate community resilience

Community resilience is becoming a growing concern for authorities and decision makers. This paper introduces two indicator-based methods to evaluate the resilience of communities based on the PEOPLES framework. PEOPLES is a multi-layered framework that defines community resilience using seven dimensions. Each of the dimensions is described through a set of resilience indicators collected from literature and they are linked to a measure allowing the analytical computation of the indicator’s performance. The first method proposed in this paper requires data on previous disasters as an input and returns as output a performance function for each indicator and a performance function for the whole community. The second method exploits a knowledge-based fuzzy modeling for its implementation. This method allows a quantitative evaluation of the PEOPLES indicators using descriptive knowledge rather than deterministic data including the uncertainty involved in the analysis. The output of the fuzzy-based method is a resilience index for each indicator as well as a resilience index for the community. The paper also introduces an open source online tool in which the first method is implemented. A case study illustrating the application of the first method and the usage of the tool is also provided in the paper.

Indicator-Based Geostatistical Models For Mapping Fish Survey Data

Marine research survey data on fish stocks often show a small proportion of very high-density values, as for many environmental data. This makes the estimation of second-order statistics, such as the variance and the variogram, non-robust. The high fish density values are generated by fish aggregative behaviour, which may vary greatly at small scale in time and space. The high values are thus imprecisely known, both in their spatial occurrence and order of magnitude. To map such data, three indicator-based geostatistical methods were considered, the top-cut model, min–max autocorrelation factors (MAF) of indicators, and multiple indicator kriging. In the top-cut and MAF approaches, the variable is decomposed into components and the most continuous ones (those corresponding to the low and medium values) are used to guide the mapping. The methods are proposed as alternatives to ordinary kriging when the variogram is difficult to estimate. The methods are detailed and applied on a spatial data set of anchovy densities derived from a typical fish stock acoustic survey performed in the Bay of Biscay, which show a few high-density values distributed in small spatial patches and also as solitary events. The model performances are analyzed by cross-validating the data and comparing the kriged maps. Results are compared to ordinary kriging as a base case. The top-cut model had the best cross-validation performance. The indicator-based models allowed mapping high-value areas with small spatial extent, in contrast to ordinary kriging. Practical guidelines for implementing the indicator-based methods are provided.

Demystifying vulnerability assessment of agriculture communities in the Himalayas: a systematic review

Agriculture communities in the Himalayas are disproportionately vulnerable due to the emerging challenges from climatic and non-climatic stressors. In this study, we systematically review peer-reviewed literature focused on vulnerability assessment of agriculture communities (n = 26) in the five Himalayan countries (Bhutan, China, India, Nepal, and Pakistan). We examine the yearly distribution, geographical scale, methodological approach, stress in consideration, indicators used, and assessment communication methods of the reviewed papers. Our findings indicate that vulnerability assessment of agriculture communities in the Himalayas is a recent practice, as all of the reviewed articles were published after 2007. About 62% of the assessments were conducted at local (household, community, and village) level, and few assessments at sub-national (19%) and basin (12%) levels. Indicator-based methods using primary quantitative data were most common (54%). Further, 90% of the studies addressed vulnerability to a single stressor with 50% of papers dealing with the vulnerability of agriculture communities to climate change and/or climate variability. From the analysis of the literature, it was found that multi-level, multi-stress, and comprehensive socio-ecological assessments were seldom attempted. Mostly the studies were done in isolated pockets and failed to identify the patterns of vulnerability. We advocate that to holistically understand the vulnerability-creating and differentiating mechanisms in agriculture communities, vulnerability assessment should adopt a multi-level approach by integrating both social and ecological determinants, firstly to identify the hotspots of vulnerability and then to deeply understand the root causes in the identified hotspots through integrated analysis.